Red, yellow, green: can a traffic light system help systematic reviews?
Notice bibliographique
Résumé
SIR–The article by Novak et al.1 has systematically graded evidence on all interventions for children with cerebral palsy (CP) using the GRADE2 system paired with a traffic light system that interprets the evidence grading in colour categories of green (do it), yellow (+ve probably do it; −ve probably do not do it), and red (do not do it).3 This article is creating ‘buzz’ (both negative and positive) with some negative comments voiced by health professionals providing red or yellow coded interventions, as well as questions about the validity of using this methodology to make recommendations on clinical practice. Interestingly, I have seen this article used in the first month since its publication on three occasions: (1) by an academic colleague who wished to succinctly summarize the evidence for constraint therapy and used the ‘bubble’ figures at a workshop for clinicians; (2) by a new occupational therapist working with children with CP who wanted to review the evidence for interventions; and (3) by me when asked by a journalist to comment on the use of ‘therasuits’ as an intervention for CP. It is rare to see an article get so much ‘quick’ pick-up and I interpret this as a sign that it fills a gap in our literature. I wish to share my perspectives as both an academician contributing to evidence as well as a clinician providing service for children with CP. I have taken the liberty of borrowing the authors' Evident Alert Traffic Light System and have organized my thoughts in the following categories: Green – go – positive aspects; Yellow – proceed with caution; and Red – stop – negative aspects (fatal flaws) of the article. Green (positive). (1) Provision of a summary of the state of evidence on interventions for children with CP allowing easy access for all audiences. (2) Utilization of the GRADE system which incorporates a clinical context into grading the evidence (e.g. balancing of benefits with risks). (3) A relative comparison of the effectiveness of different interventions. (4) Use of the traffic light system which allows easy interpretability. This is a particular advantage to busy clinicians who wish to have a quick overview in a manner that is easy to remember. (5) Categorization of interventions according to the International Classification and Functioning, Disability and Health framework, which helps readers to understand the primary intent of the intervention (e.g. activity, participation, environment). (6) The use of a helicopter perspective on all interventions which identifies where we need to go to move the field forward. Yellow (caution). (1) Given the complexity of the GRADE system and the ability to provide a grade even though the quality of evidence may be low, further details on the expert panel would be helpful. Was the expert panel multidisciplinary enough to interpret evidence with adequate expertise? (2) Enhanced discussion on the limits to published evidence to further guide readers. For example, greater details on the challenges of doing randomized clinical trials for certain interventions (e.g. surgical), and the potential bias of industry driven research could be provided. (3) For some interventions the authors interpret evidence outside of the CP literature (e.g. anticonvulsants) to make recommendations for CP. The authors need to ensure that this is applied (where applicable) to all interventions that are graded. (4) A helicopter view has the potential to miss details required for clinical context and hence clinical interpretation should be made in a conservative manner. For example, hip surveillance received a green rating but an orthopedic intervention to manage hip subluxation received a yellow rating. One of the criteria for surveillance is the need for established effective interventions. Certainly the expert clinician would accept orthopaedic interventions to maintain the integrity of the hip joint as the current criterion standard of treatment. This example highlights the difference between evidence-based practice where clinicians provide interventions that have an established evidence base, versus my strong preference for evidence-informed practice where clinicians develop treatment recommendations by integrating knowledge of evidence with clinical expert opinion to establish a well-rounded practice base. The DELPHI methodology is useful for this.4 Red (fatal flaws). None identified. Overall, this article represents a step forward for evidence-informed practice, representing an innovative and accessible way of comparing evidence for interventions. This methodology has many ‘green’ and some yellow ‘cautionary’ aspects, but no ‘red’ fatal flaws. Further work on ways to incorporate expert opinion using methodology such as the DELPHI approach may help to strengthen this article's method for making recommendations on clinical service provision. Both the article and the discussion that is ensuing will help to move the field of evidence-informed practice forward.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,393 | 0,766 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,004 |
| Méta-épidémiologie (sens large) | 0,008 | 0,006 |
| Bibliométrie | 0,031 | 0,032 |
| Études des sciences et des technologies | 0,004 | 0,009 |
| Communication savante | 0,020 | 0,029 |
| Science ouverte | 0,006 | 0,013 |
| Intégrité de la recherche | 0,013 | 0,010 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,022 | 0,005 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».